ggplot2 R package for Data Visualization

ggplot() function

Let’s look at an example diamonds data that comes with the ggplot2 package. But first, let’s load the the package to our session using the library() function. If you have not yet installed the ggplot2 package, you should do this first (you only have to do this once). Recall the magic command is: install.packages(““)

Some information about the diamonds dataset :

  • ~54,000 round diamonds from http://www.diamondse.info/
  • Variables:
    • carat, colour, clarity, cut
    • total depth, table, depth, width, height
    • price
  • A question of interest: What is the relationship between carat and price, and how does it depend on other factors?

Data on diamonds

Load the diamonds data set, get the dimensions, and look at the first few lines

data(diamonds, package="ggplot2")
dim(diamonds)
# [1] 53940    10
head(diamonds)
# # A tibble: 6 × 10
#   carat cut       color clarity depth table price     x     y     z
#   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
# 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
# 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
# 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
# 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
# 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
# 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

Refresher: Default R plot of price versus carat

Recall using the default settings with the plot() function

plot(diamonds$carat,diamonds$price) # x-variable first in this notation
Here is a caption.

Here is a caption. 

# or
plot(price~carat, data=diamonds) # an alternative way: this is y against x
Here is a caption.

Here is a caption.

Note: If you want to change the figure size and add a figure caption in Rmd, you can specify the fig.height, fig.width, fig.cap options inside curly brackets at the beginning of your R code chunk. See the full set of options here Bonus: If you don’t want to set the size each time you generate a plot, you can insert the options, e.g., fig_width fig_height in your YAML chunk at the beginning of your Rmd file.

Default ggplot price versus diamonds

Here comes ggplot! Using the default settings in ggplot()

theme_set(theme_bw())
library(ggplot2)
ggplot(diamonds, aes(x=carat,y=price)) + 
                          geom_point() + 
                          labs(y = 'price $', x = 'carat value')

At first glance

  • The default option in ggplot looks nicer!
  • ggplot’s syntax looks weird, especially if you’re not very familiar with lattice
  • Note: It is possible to manipulate the plot() options to get a plot similar to ggplot (and maybe even a better one), but that would require a lot of extra coding!!
  • The ggplot syntax also makes plotting more structured and easier to update

ggplot syntax

ggplot(diamonds, aes(x=carat,y=price)) + geom_point()

The first component (before the “+”) calls the ggplot function, and the data with x-y varibles. The second component (after the first “+”) tells ggplot what type of plot you want, e.g., geom_bar/geom_hist/geom_boxplot * Possible to add other lines for more customization on the plot, e.g., title, label for the axes, etc. ** https://rkabacoff.github.io/datavis/ a whole book on ggplot2 customization!!

geom_boxplot

Very easy to switch to a boxplot. We can use geom_boxplot to create boxplots when one variable is continuous and the other is a factor.

ggplot(diamonds, aes(x=cut,y=price)) + geom_boxplot()

Changing the aesthetics

You can control the aesthetics of each layer, e.g. colour, size, shape, alpha (opacity) etc. https://ggplot2.tidyverse.org/reference/geom_point.html

ggplot(diamonds, aes(carat, price)) + geom_point(col = "blue")

A few more examples

Changing the alpha level

ggplot(diamonds, aes(x=carat,y=price)) + geom_point(alpha = 0.2)

A few more examples

Changing the point size

ggplot(diamonds, aes(x=carat,y=price)) + geom_point(size = 0.2)

A few more examples

Changing the shape and the point size

ggplot(diamonds, aes(x=carat,y=price)) + geom_point(shape = 2,size=0.4)

Combining layers

The real power of ggplot is its ability to combine layers

ggplot(diamonds, aes(x=carat,y=price)) + geom_point(size = 0.2) +
  geom_smooth()

Transformations

In this case (and many other situations) a log transformation may allow for the relationships between variables to be clearer. Can use coord_trans()

ggplot(diamonds, aes(carat, price)) + geom_point(size = 0.5) +
coord_trans(x = "log10", y = "log10")

Adding information for a third variable

We can color by a factor variable (not that it’s useful for this example!)

ggplot(diamonds, aes(carat, price, colour=color)) + geom_point() + 
    coord_trans(x = "log10", y = "log10")

Can also color by a continuous variable (not really useful for this example too, but here it is so you are familiar with the syntax:)

ggplot(diamonds, aes(carat, price, colour=depth)) + geom_point() + 
    coord_trans(x = "log10", y = "log10")

Conditional plots:

In some cases, it may be more useful to get separate plots for each category of the third variable, to understand conditional relationships

ggplot(diamonds, aes(carat, price)) + geom_point() +
  facet_wrap(~color, ncol=4)

Alternatively, you can use facet_grid, which also allows more than 1 conditioning variable (tables of plots)

ggplot(diamonds, aes(carat, price)) + geom_point() +
 facet_grid(~color, labeller=label_both)

A final note about syntax

There are actually many ways to get the same plot! The following commands will produce the same plot:

  • ggplot(diamonds, aes(price, carat)) + geom_point()
  • ggplot() + geom_point(aes(price, carat), diamonds)
  • ggplot(diamonds) + geom_point(aes(price, carat))
  • ggplot(diamonds, aes(price)) + geom_point(aes(y = carat))
  • ggplot(diamonds, aes(y=carat)) + geom_point(aes(x = price))

A final summary of syntax:

ggplot(diamonds) + geom_point(aes(price, carat))

Histograms

Let’s make a histogram.

ggplot(diamonds, aes(depth)) + geom_histogram()

Notice the difference in the aes call; boxplot is really designed for multiple categories!

Histograms

Tthe default options in histogram may not be sensible, and you often need to adjust the binwidth and xlim

ggplot(diamonds, aes(depth)) + geom_histogram(binwidth=0.2) + xlim(56,67)

Boxplots with multiple categories

A better use of boxplot is when we want to compare distributions of a quantitative variable across categories of a factor variable, as previously discussed

ggplot(diamonds, aes(cut, depth)) + geom_boxplot()

Histograms with multiple categories

We can also get multiple histograms, though we need to either display them separately (less useful when comparing)

ggplot(diamonds, aes(depth)) + geom_histogram(binwidth = 0.2) + 
    facet_wrap(~cut) + xlim(56, 67)

Overlaying Histograms

Or, you can overlay the historgrams

ggplot(diamonds, aes(depth, fill=cut)) + 
    geom_histogram(binwidth=0.2,colour="grey50",alpha=.4,position="identity") + xlim(56,67)

Cheat sheet

We are covering only a few of the many plot types that can be greated with the ggplot2 package.

For a more comprehensive view of ggplot2, take a look at the ggplot2 Cheat sheet

Maps

load United States state map data

#install.packages('maps') # you only need to do this once. maps package includes various maps that we can use.
#install.packages('sf') # you only need to do this once
library(maps)     # Provides latitude and longitude data for various maps
library(sf)
# read the state population data
MainStates <- map_data("state")

#plot all states with ggplot2, using black borders and light blue fill
ggplot() + 
  geom_polygon( data=MainStates, aes(x=long, y=lat, group=group),
                color="black", fill="lightblue" ) +
                coord_sf(crs = st_crs(4326)) # projection